Computed-tomography (CT) imaging is a widely used form of medical imaging which uses X-rays to obtain three-dimensional image data. A CT image data set obtained from a CT scan may comprise a three-dimensional array of voxels, each having an associated intensity which is representative of the attenuation of X-ray radiation by a respective, corresponding measurement volume. The attenuation of X-ray radiation by the measurement volume may be expressed as an intensity value or CT value in Hounsfield units (HU), where 0 HU is the CT value of water.
Applications for diagnosing coronary artery disease using CT are being developed. Such applications may be used to determine the extent of any atherosclerotic heart disease present in the vessels.
It is well-known to use a contrast agent to increase the intensity of blood vessels as viewed in a CT image. A current technique for diagnosing coronary artery disease uses a non-contrast cardiac scan (which may be called a non-enhanced or pre-contrast scan and which in some circumstances may comprise a calcium scoring scan) and a contrast-enhanced cardiac scan (which may be called a post-contrast scan). The two scans are registered together (for example, using automated non-linear or linear registration processes) and a derived scan comprising subtraction data is generated based upon the difference between the intensity data values at each corresponding position in the scans. The subtraction data can be used to assess the condition of the vessels.
In a single pre-contrast calcium scoring scan, calcifications and stents may be visible, but the vessels may be indistinguishable from other soft tissue. In a single contrast-enhanced CT scan, the measured Hounsfield values for contrast-enhanced blood may overlap with the Hounsfield values for calcifications and stents. If an image derived from contrast-enhanced scan data is displayed using standard window level or color mapping techniques, it may be difficult to distinguish between contrast-enhanced blood and calcifications or stents.
If the contrast-enhanced blood can be viewed alone, then it may be possible to determine the extent of coronary disease and measure vessel stenosis (abnormal constriction or narrowing of a vessel). Subtracting registered non-contrast data from the contrast-enhanced data may remove features that are common to the contrast-enhanced and non-contrast scan, for example bone or soft tissue, to leave parts of the contrast image that have been enhanced by the contrast agent.
FIG. 1 is a flowchart illustrating a known subtraction method. At stage 10, a non-contrast volume (pre-contrast study 100) and a contrast-enhanced volume (post-contrast study 101) are registered using a non-rigid registration technique. Deformation field 102 is obtained from the registration. Deformation field 102 relates coordinates in the coordinate system of the non-contrast volume 100 to coordinates in the coordinate system of the contrast-enhanced volume 101.
At stage 12, a registered non-contrast volume (registered pre-contrast study 103) is calculated using the deformation field 102 and the non-contrast volume 100. The locations of samples in the registered non-contrast volume 102 correspond with the location of samples of the contrast-enhanced volume 101.
At stage 14, a subtraction volume 104 is generated by subtracting Hounsfield values of samples in the registered non-contrast volume 103 from Hounsfield values of corresponding samples in the contrast-enhanced volume 101.
At stage 16, the subtraction volume 104 is rendered in a renderer to produce a rendered subtraction image 105.
The rendered subtraction image 105 may be an image in one of many visualization view types. For example, the rendered subtraction image 106 may be a 2D slice, a 2D multi-planar rendering (MPR) image, a MPR plus slab image, a curved MPR image, a shaded volume rendering (SVR) image or an Intensity Projection (IP) image (for example a Maximum Intensity Projection image, Minimum Intensity Projection image or Average Intensity Projection image).
The rendered subtraction image 105 is a grayscale image where the grayscale value of a given pixel is representative of the difference between the intensity of the point or points represented by that pixel in the registered non-contrast volume and the intensity of the point or points represented by that pixel in the contrast-enhanced volume. The subtraction volume 104 contains less information than the original volumes 100, 101 because the grayscale value of each pixel is representative of a difference value. For example, a difference value of 5 may result from intensity values of 5 and 10 HU, or from intensity values of 105 and 110 HU.
Subtraction images may be difficult to learn to read. When reading images of a single channel grayscale subtraction volume, there may be cases where it is difficult to distinguish between a real occlusion or stenosis in the vessel and an artifact caused by a failure in the registration. FIG. 2 shows a subtraction image on which has been marked a dashed ellipse 17 containing a part of the subtraction image that is representative of a heavily calcified coronary artery, which may be difficult to read because of the difficulty in distinguishing between stenosis and image artifacts. Determining whether contrast is flowing through a vessel can be difficult when there is a lot of calcification.
Segmentation is the process of identifying pixels or voxels representing a given structure in an image, and may further include separating the pixels or voxels from the rest of the image. The structure may be, for example, an anatomical structure such as a vessel or an organ, or an artificial structure such as a stent. The identification and/or separation of pixels or voxels representing the structure may facilitate further processing of information relating to the structure, for example, measurement of the structure, or rendering the structure in a way that is distinct from other structures in the image.
Segmentation of blood vessels may be used in identifying diseased regions of the vessels, for example regions displaying calcifications. Accurate segmentation of the vessel lumen and vessel wall may be required in order to perform stenosis measurements, for example in the coronary arteries.
It may be difficult to obtain accurate and robust vessel segmentation, particularly in coronary vessels. Smaller vessels may be near the limit of detector resolution, and may therefore be difficult to segment. Segmentation of the vessel wall may be difficult, for example because of similarities between the intensities of vessel wall and of other soft tissue. Some known segmentation methods for classifying vessels in subtraction may depend on very accurate registration and may have no awareness of registration errors.
Automatic or semi-automatic vessel tracking algorithms may be used to track vessels within imaging data. However, tracking and segmenting vessels in contrast-enhanced data may be difficult because of the similarity between the intensity of contrasted blood in the vessel lumen and the intensity of calcium in calcifications.
Blooming may obfuscate calcifications, and may complicate lumen classification, particularly in the presence of calcifications or stents. Blooming is a type of image artifact which makes a region of calcification (or a stent) look larger than its true physical extent. Blooming may be due to a combination of artifacts such as beam hardening and motion. It is known that blooming may be more significant in contrast-enhanced images than in non-contrast images.
FIGS. 3(a) and 3(b) are representative of the results of a known coronary vessel segmentation method that has been performed on a contrast image. A large calcium deposit is present in the vessel to be segmented.
FIGS. 3(a) and 3(b) show two different views of a set of coronary computed tomography angiography (CCTA) image data showing a coronary vessel in which a contrast agent is present. FIG. 3(a) shows a straightened vessel view along the vessel centerline. FIG. 3(b) shows a cross-section of the vessel taken perpendicular to the centerline. A large calcium deposit 20 can be seen in both views as an area of high intensity.
A lumen segmentation has been performed on the image data corresponding to FIGS. 3(a) and 3(b), using the known coronary vessel segmentation method. The bounds of the segmentation are shown in each of FIGS. 3(a) and 3(b) as lines 18.
The extent of the lumen was also assessed by a clinician using the same image data. The automatically segmented lumen boundary of line 18 was found to be smaller than the lumen area that was assessed by the clinician. The size of the vessel lumen was underestimated in this known method of segmentation of the contrasted data set, due to the presence of the large calcium deposit 20.
Clinicians may not fully trust some current methods of automated algorithmic segmentation. If automatic or semi-automatic vessel segmentation underestimates or overestimates the lumen then the results of the segmentation algorithm may not provide a benefit to the clinician.